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Twelve healthy boys (age: 8–12 y), 12 untrained men (19–23 y), and 13 endurance male athletes (19–27 y) volunteered to participate in the study. To be included, boys and untrained men had to perform recreational physical activity for ≤4 h per week and to be free of any medical contra-indication to physical activity. Boys were prepubertal based on the assessment of their somatic maturity (see below). None of them were involved in any vigorous physical activity or engaged in a specific aerobic training program. Boys were recruited from primary and secondary schools while untrained men were university students. Their recreational physical activities were Alpine skiing, snowboarding, sailing, skateboarding, climbing, etc. In contrast, endurance-trained adults were engaged in long distance physical activities for ≥6 times a week for at least 2 years and were national-level competitive athletes (i.e., long-distance runners, cyclists, and triathletes). They were recruited from local sports clubs (athletics, triathlon, and cycling). This study was approved by a local Institutional Ethics Review Board. The study was conducted in conformity with the policy statement regarding the use of human subjects by the Declaration of Helsinki. All experimental procedures were clearly explained to the participants, who then gave written consent before the commencement of testing. Written consent was also obtained from parents/guardians before children were accepted into the study.

All participants were tested in two experimental sessions separated by at least 48 h. During the first experimental session, anthropometric characteristics, body composition, maturation status, and the power at maximum O₂ uptake (PVO_2max) were evaluated. Furthermore, the participants had to perform two sprints (~7 s) on a cycle ergometer (Cyclus model II, MSE Electronic Medical, Leipzig, Germany) separated by 1 min of recovery against a resistance corresponding to 7.5% body mass (BM). These sprints served to familiarize the volunteers with the experimental procedures of the second session. During the second visit, the participants were asked to perform a Wingate cycle test to determine the maximal anaerobic power (P_max), the fatigue rate (i.e., the relative decrement in power output), the relative (net) energy contribution derived from oxidative metabolism and the post-exercise recovery rates of blood lactate concentration, HR, and O₂ uptake. The Wingate cycle test was chosen because it allows the simultaneous investigation of cardio-respiratory, metabolic, and muscular components of fatigue during a whole-body dynamic activity and is a common and well-learned activity within the population. Furthermore, the Wingate cycle test was found to be highly reliable from one session to another in children and adults for the evaluation of high-intensity exercise performance indices (Hebestreit et al., 1993).

Body mass was measured to the nearest 0.1 kg using a digital weight scale (TANITA, BC-545N, Japan) and standing height was assessed using a portable stadiometer with the participants barefoot (TANITA, HR001, Japan). Sitting height was also measured with the stadiometer while the participants sat on the floor with their back against a wall. Body Mass Index (BMI) was subsequently calculated by the equation BM (kg)/height² (m²). Skinfold thickness was measured in duplicate at the triceps and subscapular sites using a Harpenden caliper (Baty International, Burgess Hill, UK). The measurements were taken by the same investigator on the right side of the body to reduce variability in the results. Body fat (BF, %) was assessed using Slaughter's equations (Slaughter et al., 1988). Body fat (kg) was calculated by multiplying BF (%) by BM (kg). Fat-free mass (FFM, kg) was determined by subtracting BF (kg) from BM (kg).

Age from peak height velocity (APHV) was used to assess somatic maturity and determined by using height, sitting height and BM. Its calculation was only done in children and was based on sex-specific regression equations according to the method proposed by Mirwald et al. (2002).

PVO_2max was assessed using a submaximal graded test on a cycle ergometer (Cyclus model II, MSE Electronic Medical, Leipzig, Germany). Workloads were initially set at 30, 100, and 130 W and then increased by 15, 30, and 30 W every 3 min in prepubertal children, untrained adults, and well-trained adult endurance athletes, respectively. The pedaling rate was constant and self-selected by the participants between 50 and 90 rpm. The test was designed to have one warm-up stage and three to four additional submaximal stages leading HR to at least 160 bpm. HR was measured during the last minute of each stage using the Polar recorder linked to the cycle ergometer (Polar Electro, Kempele, Finland). If the participant's HR did not reach a plateau or the participant did not maintain cadence, the measurement was considered to be invalid. PVO_2max was then assessed from individual linear regressions between power output and HR and by calculating the power output at the age-predicted maximal HR (HR_max). The squared Bravais-Pearson correlation coefficients of these linear relationships ranged between 0.91 and 0.99 (mean ± SD: 0.98 ± 0.02). HR_max was assessed using the equation formulated by Shargal et al. (2015), which was validated from a large cohort of healthy males aged between 10 and 80 years:

Furthermore, VO_2max was assessed using the following equation:

where E is the caloric equivalent of oxygen for a respiratory exchange ratio above 1.0, i.e., 21131 J·L⁻¹, and R the cycling efficiency (%). A delta efficiency of 23% was chosen for all the volunteers since the literature reports no apparent difference during cycling between 10.5-year-old boys (23.2%), 21.3-year-old untrained men (22.5%), and 25.6-year-old well-trained endurance triathletes and cyclists (22.7%) (Rowland et al., 1990; Louis et al., 2012).

The testing session started with a rest period of 10 min in a sitting position on the cycle ergometer. The saddle height was set at 107% of trochanteric leg length to give optimal comfort to each participant (Hamley and Thomas, 1967). Afterwards, the participants were asked to perform a standardized warm-up of 4 min cycling against a resistance that was adjusted to 2% of their BM. At the end of the second, third, and fourth minutes, they also had to perform one sprint lasting 5 s against a resistance corresponding to 7.5% BM. After a subsequent 5-min rest, the participants were instructed to perform a Wingate test that consisted of pedaling as fast as possible for 30 s against a resistance corresponding to 0.075 g·kg⁻¹ BM (Bar-Or, 1987). This braking load was chosen as it does not appear to influence P_max or fatigue rate during the Wingate test, when compared to heavier loads (e.g., 11% BM), in either untrained adults or less-powerful athletes such as endurance athletes (Jaafar et al., 2016). Furthermore, it corresponds to an optimal braking load to produce P_max and mean power output (P_mean) during a Wingate test in children aged from 6 to 12 years (Carlson and Naughton, 1994). Prior to each sprint, the experimenter announced a countdown of “5-4-3-2-1-GO” during which the participants were instructed to put the right crank arm 45° past top position. After test termination, the subjects were supervised during a 20-min recovery period in a sitting position. The participants were instructed not to engage in any strenuous activity on the day preceding the exercise test.

Data were screened for normality of distribution and homogeneity of variances using a Shapiro-Wilk normality test and the Barlett's test, respectively. One-way (group) ANOVA was used to compare age, anthropometric characteristics, performance outcomes (e.g., P_max, PVO_2max, P_max-to-PVO_2max ratio) and blood lactate kinetics parameters (e.g., A₁, A₂, γ₁, γ₂, La]_pk, tLa]_pk) between groups. When ANOVA revealed a significant effect, a Newman-Keuls post-hoc test was applied to test the discrimination between means. Furthermore, a two-way (group × time) ANOVA with repeated measures was used to analyze the relative energy contribution from oxidative metabolism throughout the Wingate test as well as the post-exercise recovery kinetics of HR and O₂ uptake. When ANOVA revealed a significant main or interaction effect, a Newman-Keuls post-hoc test was applied to test the discrimination between means. The size effect and statistical power have also been reported when significant main or interaction effects were detected. The size effect was assessed using the partial eta-squared (η²) and ranked as follows: ~0.01 = small effect, ~0.06 = moderate effect, ≥0.14 = large effect (Cohen, 1969). A linear regression model between the fatigue index and the P_max-to-PVO_2max ratio was fitted by the least squares method considering all the volunteers, and a squared Bravais-Pearson correlation coefficient (r²) of this linear regression model was calculated. The limit for statistical significance was set at p < 0.05. Statistical procedures were performed using Statistica 8.0 software (Statsoft, Inc., USA). Results were presented in the text, tables and figures as mean ± SD.

The physical characteristics of participants are presented in Table 1. ANOVA revealed significant differences between the different groups for age [F_{(2, 34)} = 131.1, p < 0.001, η² = 0.88, power = 1.0], height [F_{(2, 34)} = 110.2, p < 0.001, η² = 0.87, power = 1.0], BM [F_{(2, 34)} = 123.2, p < 0.001, η² = 0.88, power = 1.0], BMI [F_{(2, 34)} = 61.2, p < 0.001, η² = 0.78, power = 1.0], BF (%) [F_{(2, 34)} = 20.4, p < 0.001, η² = 0.55, power = 0.99], FFM (kg) [F_{(2, 34)} = 183.8, p < 0.001, η² = 0.92, power = 1.0], HR_rest [F_{(2, 34)} = 33.6, p < 0.001, η² = 0.66, power = 1.0], PVO_2max (W) [F_{(2, 34)} = 118.4, p < 0.001, η² = 0.87, power = 1.0], PVO_2max/BM (W·kg⁻¹) [F_{(2, 34}) = 26.1, p < 0.001, η² = 0.61, power = 0.99], and the P_max-to-PVO_2max ratio [F_{(2, 34)} = 38.5, p < 0.001, η² = 0.69, power = 1.0]. Post-hoc tests showed no significant difference for age and height between untrained adults and well-trained adult endurance athletes. However, untrained adults displayed significantly higher values for BM (p < 0.05), BMI (p < 0.01), BF (p < 0.05), FFM (p < 0.05), HR_rest (p < 0.001) when compared to their well-trained endurance counterparts. In contrast, as expected, PVO_2max (W and W·kg⁻¹) was significantly higher in well-trained adult endurance athletes than untrained adults (p < 0.001 for both).

Furthermore, stature, BM, BMI, FFM, and PVO_2max (W) were significantly lower in prepubertal children than untrained adults and well-trained adult endurance athletes (p < 0.001 for each). In contrast, prepubertal children displayed significantly higher values for BF and HR_rest than their older trained and untrained counterparts (p < 0.05 at least). However, PVO_2max/BM (W·kg⁻¹) was not significantly different between prepubertal children and untrained adults.

Interestingly, no significant difference was observed in the P_max-to-PVO_2max ratio between prepubertal children and well-trained adult endurance athletes; however, the P_max-to-PVO_2max ratio was significantly higher in untrained adults than prepubertal children and well-trained adult endurance athletes (p < 0.001 for both) (Table 1).

Performance outcomes obtained during the Wingate test are displayed in Figure 1 and Table 2. ANOVA revealed significant differences between the groups for P_max (W) [F_{(2, 34)} = 178.8, p < 0.001, η² = 0.91, power = 1.0], P_max/BM (W·kg⁻¹) [F_{(2, 34)} = 49.6, p < 0.001, η² = 0.74, power = 1.0], tP_max [F_{(2, 34)} = 12.4, p < 0.001, η² = 0.42, power = 0.99], P_mean (W) [F_{(2, 34)} = 201.4, p < 0.001, η² = 0.92, power = 1.0], P_mean/BM (W·kg⁻¹) [F_{(2, 34)} = 46.9, p < 0.001, η² = 0.73, power = 1.0], FI [F_{(2, 34)} = 12.6, p < 0.01, η² = 0.43, power = 0.99], and RPE [F_{(2, 34)} = 3.7, p < 0.05, η² = 0.18, power = 0.64]. In contrast, ANOVA showed no significant effect for HR_pk between the groups.

More specifically, untrained adults displayed significantly higher values for P_max (W), P_mean (W), and FI (%) than well-trained adult endurance athletes (p < 0.05 at least). However, P_max/BM (W·kg⁻¹), P_mean/BM (W·kg⁻¹), tP_max, and RPE were not significantly different between untrained and endurance trained adults. Furthermore, children displayed significantly lower values than untrained and trained adults for all parameters investigated (p < 0.05 at least) except for FI (%), which was similar to that of endurance-trained adults.

Regarding the relative aerobic contribution, ANOVA tended to show a group × time interaction effect [F_{(10, 170)} = 3.86, p = 0.09, η² = 0.09, power = 0.78]. The relative aerobic contribution increased progressively during the Wingate test whatever the group considered (Figure 2). However, this increment was not significantly different between prepubertal children and well-trained adult endurance athletes but significantly lower in untrained adults compared to prepubertal children over the second half of the Wingate test (p < 0.05 at least). Furthermore, well-trained adult endurance athletes displayed a significantly greater relative aerobic contribution than untrained adults over the last 10 s of the Wingate test (p < 0.05).

ANOVA revealed a significant group × time interaction throughout the post-exercise recovery period for HR [F_{(32, 544)} = 11.71, p < 0.001, η² = 0.41, power = 1.0] and VO₂ [F_{(32, 480)} = 2.24, p < 0.001, η² = 0.13, power = 0.99]. Post-hoc tests showed a faster recovery of HR in prepubertal children compared to trained adults from 45-s of recovery, and in prepubertal children compared to untrained adults from 30-s of recovery until the end of test. Furthermore, the recovery rate of HR was significantly faster in endurance-trained athletes compared to untrained adults between the first and second minutes of recovery (Figure 3).

No significant difference in the recovery rate of VO₂ was observed between prepubertal children and well-trained adult endurance athletes (Figure 4). However, VO₂ recovered faster in prepubertal children and well-trained adult endurance athletes compared to untrained male adults between 45 and 120-s of recovery.

Finally, ANOVA revealed a significant group effect for [La]₍₀₎ [F_{(2, 34)} = 5.85, p < 0.01, η² = 0.26, power = 0.84], [La]_pk [F_{(2, 34)} = 39.88, p < 0.001, η² = 0.70, power = 1.0], t[La]_pk [F_{(2, 34)} = 4.81, p < 0.05, η² = 0.22, power = 0.76], A₁ [F_{(2, 34)} = 10.08, p < 0.001, η² = 0.37, power = 0.98], A₂ [F_{(2, 34)} = 17.8, p < 0.001, η² = 0.51, power = 0.99], and γ₂ [F_{(2, 34)} = 14.31, p < 0.001, η² = 0.46, power = 0.99]. In contrast, no significant group effect was observed for [La]_rest and γ₁ (Table 3). More specifically, A₁, A₂, and t[La]_pk were similar in untrained and trained adults but higher than prepubertal children (p < 0.05 at least). Furthermore, γ₂ and [La]_pk were significantly different between groups (γ₂: children > trained adults > untrained adults; [La]_pk: untrained adults > trained adults > children).

A significant positive relationship was observed between FI (%) and the P_max-to-PVO_2max ratio considering all participants (r² = 0.54, p < 0.001) (Figure 5).

In the present study, prepubertal children displayed a lower relative decrement in power output during the Wingate test than untrained adults (−35.2 vs. 51.8%, respectively); thus they fatigued much less than their untrained older counterparts. This finding cannot be explained by the fact that children performed more (especially aerobic) physical activity since the boys and men had to meet the same strict inclusion criteria and their relative VO_2max were not significantly different (49.0 vs. 48.1 mL·min⁻¹·kg⁻¹, respectively). The finding is in accordance with the literature showing a reduced susceptibility to fatigue in prepubertal children during different whole-body, high-intensity activities such as cycling (Ratel et al., 2002), running (Ratel et al., 2004), and hopping (Lazaridis et al., 2018), or during maximal voluntary contractions under isometric (Ratel et al., 2015) and isokinetic (De Ste Croix et al., 2009) conditions. The mechanisms underpinning the differences in fatigue between prepubertal children and untrained adults are not yet fully understood but it appears that prepubertal children experience less peripheral (i.e., muscular) fatigue and potentially more central (i.e., neural) fatigue than untrained adults during high-intensity exercise (Streckis et al., 2007; Ratel et al., 2015). The greater fatigue effect on central mechanisms in prepubertal children could account for their lower fatigue at the peripheral level. Indeed, according to one theory, the central nervous system could limit the recruitment of motor units to prevent any extensive homeostasis disturbance, muscle damage, or biological harm (Noakes et al., 2005). However, direct evidence to support this assumption is still lacking. An alternative could be the lesser ability of prepubertal children to fully voluntarily activate motor units during high-intensity exercise (Dotan et al., 2012). Indeed, a lower activation level was found to be associated with a higher resistance to fatigue in adults (Nordlund et al., 2004). In the present study, the maximal voluntary activation level was not measured; however, the longer lag time to reach maximal anaerobic power in children compared to untrained adults (tP_max: 5.8 vs. 1.8 s, respectively; e.g., Figure 1, solid black line) might be suggestive that the amplitude of muscle activation was lower during the early phase of the Wingate test in children and then increased over time, similar to wind-up effects observed in other (e.g., clinical) populations (Hornby et al., 2009). Therefore, the possibility exists that the higher resistance to fatigue in children could be also ascribed to an inability to fully activate the neuromuscular system during the early phase of an explosive exercise bout. This hypothesis should be explicitly examined in future studies.

Among peripheral factors, the greater relative contribution of energy derived from aerobic metabolism in prepubertal children may account for their lower fatigability during high-intensity exercise. The significant positive relationship found in the present study between the fatigue index and the P_max-to-PVO_2max ratio as well as the lower P_max-to-PVO_2max ratio in children compared to young untrained adults (~1.9 vs. 3.2, respectively) support this assertion; the lower the relative anaerobic contribution, the lower the development of fatigue during high intensity exercise in children. Furthermore, the relative oxidative contribution was found to be greater in prepubertal children than untrained adults over the second half of the Wingate test. The recovery kinetics of cardio-respiratory parameters (VO₂ and HR) and the removal ability of lactate out of the blood compartment (γ₂) were also faster following the Wingate test in prepubertal children than untrained adults. These results are consistent with previous studies describing the changes in the anaerobic-to-aerobic mechanical power ratio (P_max-to-PVO_2max ratio) during growth using either longitudinal (Falk and Bar-Or, 1993) or cross-sectional (Blimkie et al., 1986; Falgairette et al., 1991) experimental designs, despite aerobic mechanical power not being determined using direct VO_2max measurement in the current study. For instance, Falgairette et al. (1991) reported a progressive increment in the P_max-to-PVO_2max ratio until the onset of adolescence (e.g., 1.8 at 9–10 y, 2.2 at 11–12 y, and 2.8 at 14–15 y) in 144 boys, after which it remained stable until early adulthood (~3.0 in physically active but non-competitive young men in the study by Falk and Bar-Or, 1993). Furthermore, some authors have reported (i) a greater relative contribution of energy derived from oxidative metabolism during the Wingate test in 11.8-year-old boys than 16.3-year-old male adolescents (Beneke et al., 2007), and (ii) a smaller O₂ deficit incurred at the start of cycle exercise at 125% VO_2max, which called for a lesser excess post-exercise VO₂ in prepubertal children than untrained adults (Armon et al., 1991). Similarly, the recovery rate of HR was found to be faster in prepubertal children than untrained adults following high-intensity exercise lasting 1 min (Baraldi et al., 1991; Hebestreit et al., 1993). Our results are also consistent with the experimental data derived from blood measurements showing no difference in the exchange capacity of lactate from muscles into blood (γ₁) between children and untrained adults but a better ability to remove lactate from the blood compartment (γ₂) in children following the Wingate test (Beneke et al., 2005).

This more oxidative metabolic profile in prepubertal children is usually associated with a lesser accumulation of metabolic by-products (i.e., H⁺ ions, lactate, inorganic phosphate) derived from anaerobic sources in exercising muscle (Kappenstein et al., 2013). In the present study, lactate concentrations were only measured at the blood level, and the bi-exponential time function revealed a lower accumulation with a peak value appearing earlier during the post-exercise recovery period in prepubertal children, as evidenced by their lower A₁, A₂, and [La]_pk values and shorter t[La]_pk (Table 3). The underlying mechanisms of this diminished lactate response in prepubertal children has yet to be elucidated, but the pediatric literature often suggests that the lower blood/muscle lactate concentration in prepubertal children results from their lower glycolytic energy turnover (Ratel et al., 2002). Furthermore, it has been shown that the shorter t[La]_pk in children could be related to their lower accumulated lactate rate during high intensity exercise (Dotan et al., 2003).

While lactate ion accumulation itself has only a small effect on the loss of muscle power in fatigue (Allen et al., 2008), the associated accumulation of metabolic by-products such as H⁺ ions and inorganic phosphate could promote peripheral fatigue to a greater extent through an alteration of contractile processes and the excitation-contraction coupling (Allen et al., 2008). The lesser accumulation of these metabolites in children could therefore translate into a reduced fatigue at the periphery, as usually observed in this population during high-intensity exercise (Hatzikotoulas et al., 2014; Ratel et al., 2015). In addition, the lower anaerobic capacity and the greater parasympathetic reactivation of the autonomic nervous system early in the recovery period after exercise in prepubertal children could explain the faster readjustment of cardiovascular parameters (Armon et al., 1991; Ohuchi et al., 2000) and the higher removal ability of lactate from the blood compartment (γ₂).

In contrast to untrained adults, prepubertal children displayed the same metabolic profile as well-trained adult endurance athletes. The P_max-to-PVO_2max ratio was similar in children and endurance-trained athletes (1.9 vs. 2.1, respectively), and was consistent with those previously reported in well-trained adult endurance athletes who had VO_2max values between 60 and 70 mL·min⁻¹·kg⁻¹ (~2.0 for Meeuwisse et al., 1992; Hostrup et al., 2016). Furthermore, the relative energy contribution derived from oxidative metabolism during the Wingate test as well as the post-exercise recovery rate of VO₂ were similar in children and endurance adult athletes. On this basis, prepubertal children could be considered analogous to well-trained adult endurance athletes from a physiological perspective, despite them having a lower work capacity than their older counterparts, i.e., a lower mean power output during the Wingate test (Ratel and Blazevich, 2017). Surprisingly, the results of the present study also showed a faster post-exercise HR recovery rate in prepubertal children than endurance adult athletes. This unexpected result could be ascribed to a greater parasympathetic reactivation of the autonomic nervous system in the early recovery period in prepubertal children than well-trained adult endurance athletes, owing to a greater central cholinergic modulation of HR (Ohuchi et al., 2000). However, further studies comparing the low-frequency and high-frequency components of HR variability in the early recovery period between both populations are required to test this assumption. Furthermore, the results of the present study indicated a lack of difference in γ₁ and a higher γ₂ in children than well-trained adult endurance athletes, suggesting that prepubertal children could have a better capacity to remove lactate from the blood compartment than their older trained counterparts. This difference in γ₂ may be related to the shorter circulation time in children (Cumming, 1978), as evidenced by their faster HR recovery rate in the present study; the faster HR recovery rate, the higher lactate removal ability (γ₂) following the Wingate test.

On a more practical level, the similar metabolic profile of prepubertal children and well-trained adult endurance athletes translated into comparable fatigue rates during the Wingate test (−35.2 vs. −41.8%, respectively) despite children taking more time to reach their maximal anaerobic power output. This is consistent with experimental data obtained by Hebestreit et al. (1993) and Harbili (2015) who indirectly showed comparable fatigue rates during the Wingate test between prepubertal boys (−44%) and professional male road cyclists of Olympic and national levels (−43%). Based on these data, it is presumed that the greater reliance on oxidative energy sources and the lower relative anaerobic contribution in well-trained adult endurance athletes (Pesta et al., 2013) translate into a lower peripheral (i.e., muscular) fatigue, as usually observed in prepubertal children compared to untrained adults during repeated maximal voluntary muscle contractions (Hatzikotoulas et al., 2014; Ratel et al., 2015). However, until now the central and peripheral components of neuromuscular fatigue during high-intensity exercise in well-trained adult endurance athletes have only been compared to those of explosive power-trained athletes (Garrandes et al., 2007) and not to untrained adults. Further studies comparing the peripheral and central components of neuromuscular fatigue between prepubertal children, well-trained adult endurance athletes and untrained adults are therefore required to check this assumption.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.